Receding Horizon and Optimization-based Control for UAV path planning with Collision Avoidance

This paper proposes an online energy-efficient path planning approach for UAVs in complex environments. The path planning problem is formulated as a minimization optimization problem based on Mixed Integer Linear Programming (MILP), where a cost function is designed to minimize energy consumption while ensuring terrain obstacle avoidance within a limited detection range. To achieve this, we apply a Receding Horizon Control (RHC) and optimization approach. The entire path is divided into segments or sub-paths, with constraints in place to prevent collisions with obstacles. This proposed optimization approach enables fast navigation through dense environments, ensuring a collision-free path. For further optimizing the path for energy, a path smoothing strategy is introduced to reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with a high risk of collisions with obstacles.